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• W425670577 abstract "Bayesian methods have in recent years become popular when consider- ing problems in geosciences, such as sequential data assimilation in high dimen- sions. Ensemble based Monte Carlo methods, such as the Ensemble Kalman Filter (EnKF), are attractive to use because they are easy to implement and computation- ally fast. However, high computational demands will often restrict the ensemble size. Problems resulting from estimation uncertainty and dependencies between the ensemble members can therefore occur. As a result, the traditional EnKF updat- ing schemes can lead to unreliable predictions with a severe underestimation of the prediction interval. In this paper, we introduce alternative EnKF updating schemes based on Bayesian regression techniques. The main idea is to replace the tradi- tional plug-in estimate of the Kalman gain matrix with individual realisations from a matrix-variate distribution for each updated ensemble member. We evaluate the performance of the suggested schemes through simulation on synthetic case stud- ies. The results reveal that we can dramatically improve the accuracy of the forecast and predictions intervals, especially for small ensemble sizes." @default.
• W425670577 created "2016-06-24" @default.
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• W425670577 date "2010-01-01" @default.
• W425670577 modified "2023-09-26" @default.
• W425670577 title "Ensemble Kalman filtering in a Bayesian regression framework" @default.
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• W425670577 hasPublicationYear "2010" @default.
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